Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems
Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems
A novel semi-blind space-time equaliser (STE) is proposed for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, are first utilised to provide a rough initial least squares estimate of the STE's weight vector. A concurrent gradient-Newton constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast to the minimum mean square error STE solution. Simulation results confirms that the convergence speed of this semi-blind adaptive algorithm is very close to that of the training-based recursive least squares algorithm.
3969-3974
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
August 2009
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Sheng and Hanzo, Lajos
(2009)
Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems.
IEEE Transactions on Wireless Communications, 8 (8), .
Abstract
A novel semi-blind space-time equaliser (STE) is proposed for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, are first utilised to provide a rough initial least squares estimate of the STE's weight vector. A concurrent gradient-Newton constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast to the minimum mean square error STE solution. Simulation results confirms that the convergence speed of this semi-blind adaptive algorithm is very close to that of the training-based recursive least squares algorithm.
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TWC09-8.pdf
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Published date: August 2009
Organisations:
Southampton Wireless Group
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Local EPrints ID: 267789
URI: http://eprints.soton.ac.uk/id/eprint/267789
PURE UUID: 29fdb401-e6e7-4648-b8e5-76a18c726f3f
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Date deposited: 19 Aug 2009 08:24
Last modified: 18 Mar 2024 02:34
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Author:
Sheng Chen
Author:
Lajos Hanzo
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